Computer Engineering Commons^™

Accelerating Pattern Recognition Algorithms On Parallel Computing Architectures, Kenneth Rice

All Dissertations

The move to more parallel computing architectures places more responsibility on the programmer to achieve greater performance. The programmer must now have a greater understanding of the underlying architecture and the inherent algorithmic parallelism. Using parallel computing architectures for exploiting algorithmic parallelism can be a complex task. This dissertation demonstrates various techniques for using parallel computing architectures to exploit algorithmic parallelism. Specifically, three pattern recognition (PR) approaches are examined for acceleration across multiple parallel computing architectures, namely field programmable gate arrays (FPGAs) and general purpose graphical processing units (GPGPUs).
Phase-only filter correlation for fingerprint identification was studied as the first …

Parallel Implementation Of The Singular Value Decomposition Using Opencl, Bhushan Rayrikar

All Theses

General-Purpose Graphics Processing Units (GPGPUs) have massively parallel computational capabilities. Low cost and ease of programming make them a popular choice over other parallel architectures such as large clusters and accelerators such as Field-Programmable Gate Arrays (FPGAs). Mature programming frameworks for GPGPUs, such as CUDA from Nvidia and OpenCL from the Khronos Group, reduce the learning curve and development time for programming GPGPU architectures. OpenCL, a relatively new industry standard for parallel computing makes it possible to write a single program for heterogeneous platforms that is portable across multiple platforms including GPGPUs and multi-core processors with minimal coding modifications.
GPGPU …

Power Management For Gpu-Cpu Heterogeneous Systems, Xue Li

Masters Theses

In recent years, GPU-CPU heterogeneous architectures have been increasingly adopted in high performance computing, because of their capabilities of providing high computational throughput. However, current research focuses mainly on the performance aspects of GPU-CPU architectures, while improving the energy efficiency of such systems receives much less attention. There are few existing efforts that try to lower the energy consumption of GPU-CPU architectures, but they address either GPU or CPU in an isolated manner and thus cannot achieve maximized energy savings. In this paper, we propose GreenGPU, a holistic energy management framework for GPU-CPU heterogeneous architectures. Our solution features a two-tier …

Molecular Dynamics Simulation Based On Hadoop Mapreduce, Chen He

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Molecular Dynamics (MD) simulation is a computationally intensive application used in multiple fields. It can exploit a distributed environment due to inherent computational parallelism. However, most of the existing implementations focus on performance enhancement. They may not provide fault-tolerance for every time-step.

MapReduce is a framework first proposed by Google for processing huge amounts of data in a distributed environment. The simplicity of the programming model and fault- tolerance for node failure during run-time make it very popular not only for commercial applications but also in scientific computing.

In this thesis, we develop a novel communication-free and each time-step fault- …

Parallel Mesh Adaptation And Graph Analysis Using Graphics Processing Units, Timothy P. Mcguiness

Masters Theses 1911 - February 2014

In the field of Computational Fluid Dynamics, several types of mesh adaptation strategies are used to enhance a mesh’s quality, thereby improving simulation speed and accuracy. Mesh smoothing (r-refinement) is a simple and effective technique, where nodes are repositioned to increase or decrease local mesh resolution. Mesh partitioning divides a mesh into sections, for use on distributed-memory parallel machines. As a more abstract form of modeling, graph theory can be used to simulate many real-world problems, and has applications in the fields of computer science, sociology, engineering and transportation, to name a few. One of the more important graph analysis …